In a typical medical application of MRI, a patient is placed within the bore of a large, donut-shaped magnet. The magnet creates a static magnetic field that extends along the long (head-to-toe) axis of the patient's body. An antenna (e.g., a coil of wire) is also positioned within the bore of the large magnet, and is used to create an oscillating radiofrequency field that selectively excites hydrogen atoms (protons) in the patient's body into oscillation. The oscillating field is then turned off, and the antenna is used as a receiving element, to detect the proton oscillations as a function of position within the body. Typically, the intensity of the oscillations is measured throughout a two-dimensional plane. When the intensities are displayed as a function of position in this plane, the result is an image that often bears a striking resemblance to the actual anatomic features in that plane.
The intensity of proton oscillations detected at a given point in the patient's body is proportional to the proton density at that point. Because different types of tissues have different proton densities, different tissue types usually have different image intensities, and therefore appear as distinct structures in the MR image. However, the signal intensity also depends on physical and chemical properties of the tissues being imaged. In a simplified model of MRI, the detected signal intensity, as a function of position coordinates x and y in the plane being imaged, is proportional to EQU (1-e.sup.-TR/T.sub.1)e.sup.-TE/T.sub.2 ( 1)
The parameters TR (recovery time) and TE (echo delay time) are under the control of the operator of the MR imaging system, and are constants for any given image. However, T.sub.1 and T.sub.2 are functions of the tissue under examination, and therefore vary with position in the x-y plane. By suitable selection of parameters TR and TE, either the T.sub.1 or the T.sub.2 term in Equation 1 can be made to dominate, thereby producing so-called "T.sub.1 -weighted" and "T.sub.2 -weighted" images, respectively.
One of the more important medical uses to which MRI has been put to date is to noninvasively scan a portion of a patient's body, in an attempt to identify benign or malignant tumors. When MRI is used in this fashion, it is necessary to have some methodology for concluding that a given portion of an MR image represents tumor, as opposed to other tissue types such as fat, cyst, etc. One known approach to identifying tissue type has been to acquire multiple MR images of the same region of the patient's body, using different imaging parameters, e.g., using different values of the TR and TE parameters. To take a simplified example, if it were known that a given tumor produced a high image intensity at a first parameter setting, a low image intensity at a second parameter setting, and a high image intensity at a third parameter setting, then a portion of a patient's body that produced that pattern of intensities (high, low, high) could be tentatively identified as tumor.
Pattern recognition approaches of this type are described in U.S. Pat. No. 5,003,979. This patent describes a system for the detection and display of lesions in breast tissue, using MRI techniques. In one described example, three different types of images are obtained for a given region, and the pixels of the image are then classified by comparing their intensity patterns to known patterns for pure tissue types, such as fat, cyst or cancer. The patent indicates that three specific types of images are adequate for statistically separating MR images of breast fat, cyst, carcinoma and fibroadenoma.
Applicants have found that in many cases, comparison of the pattern of intensities of a patient's tissue to "standard" patterns for different tissue types does not produce results of sufficient accuracy. The basic problem appears to be that there is too much variability from one patient to the next, as well as from one MRI machine to the next. For this reason, the use of standard patterns does not result in the high degree of confidence that one must have in order to forego a more certain diagnostic technique, such as biopsy. For this reason, cancer diagnosis based on MRI has not yet achieved widespread acceptance. A problem that occurs frequently in cancer treatment is detecting when a primary tumor has spread to other sites in the patient's body, to produce so-called secondary tumors, known as metastases, at those sites. Detection and correct identification of metastases, using MRI or other imaging techniques, is often complicated by the fact that a remote lesion discovered during staging could represent either a metastasis or a benign incidental finding. A number of benign lesions (such as hepatic hemangiomas and nonfunctioning adrenal adenomas) occur as frequently in patients with a known primary tumor as they do in the general population.
Resolving this dilemma requires additional imaging or biopsy, but often significant uncertainty persists. Biopsy may expose the patient to substantial risk when the lesion is in the brain or mediastinum, or when the patient has impaired hemostasis. Even when biopsy does not present a significant risk to the patient, it may be technically challenging, such as sampling focal lesions in vertebral marrow.